TETRA mobile radios interfere with electroencephalography recording equipment

We observed an anomaly in the human electroencephalogram (EEG) associated with exposure to terrestrial trunked radio (TETRA) Radiofrequency Fields (RF). Here, we characterize the time and frequency components of the anomaly and demonstrate that it is an artefact caused by TETRA RF interfering with the EEG recording equipment and not by any direct or indirect effect on the brain.

An approach to emotion recognition in single-channel EEG signals: a mother child interaction

In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology -- Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains -- Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Hemispheric Lateralization in Benign Focal Epilepsy in Childhood With Centrotemporal Spikes (BECTS)

Benign focal epilepsy in childhood with centrotemporal spikes (BECTS) is one of the most common forms of epilepsy. In adults there is a higher percentage of lateralized epileptic discharges in the left cerebral hemisphere; however, in children this pattern does not seem to have the same distribution. The objective of this study was to evaluate the lateralization of interictal spikes in children with BECTS in relation to the sex of the child and the age of onset of epilepsy. We studied the electroencephalograms (EEGs) of 114 children with a clinical diagnosis of BECTS according to ILAE. The results obtained from two EEGs, performed at intervals of 6 and 12 months, were correlated with the age of onset of the epileptic seizures and the sex of the child. There was no association between the onset of epileptic seizures and the age of the child (p=0.461). When we analyzed the relationship between laterality and sex we did not observe any difference in the first EEG (p = 0.767) results; however, in the results of the second EEG there was a difference (p = 0.002). In males, left and bilateral interictal spikes were predominant, and in females the right hemisphere showed predominant spikes and there were continuous spike-and-wave discharges during slow sleep (CSWSS). The analysis between laterality and a child`s age did not show predominant interictal spikes in the hemispheres, except in males where there were predominant multifocal and generalized spikes, but not lateralization (p=0.011). The conclusion was that in BECTS the lateralization of interictal spikes was not consistent as described in adult patients, but there was a slight left hemispheric predominance in boys and right hemispheric predominance in girls.

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

Emotional trauma and abuse in patients with psychogenic nonepileptic seizures

A history of childhood trauma and the presence of dissociative phenomena are considered to be the most important risk factors for psychogenic nonepileptic seizure disorder (PNESD). This case-control study investigated 20 patients with PNESD and 20 with temporal lobe epilepsy (TLE) diagnosed by video/EEG monitoring who were matched for gender and age. Patients with both conditions were not included in the study. Groups were evaluated for age at onset and at diagnosis, worst lifetime weekly seizure frequency, trauma history, and presence of dissociative phenomena. Age at onset (P = 0.007) and age at diagnosis (P < 0.001) were significantly higher in the PNESD group than the control group, as were the scores on the Dissociative Experiences Scale (P < 0.001) and Childhood Trauma Questionnaire (P = 0.014). Only the differences in scores on the Childhood Trauma Questionnaire subscales Emotional Neglect (P = 0.013) and Emotional Abuse (P = 0.014) reached statistical significance. Dissociative phenomena and a reported history of childhood trauma are more common in patients with PNESD than in those with TLE. However, only emotional neglect and abuse were associated with PNESD in this study. (C) 2010 Elsevier Inc. All rights reserved.

The effects of bromazepam over the temporo-parietal areas during the performance of a visuomotor task: A qEEG study

This study investigated the effects of bromazepam on qEEG when 14 healthy subjects were asked to perform a visuomotor task (i.e., motor vehicle driving task). The subjects were exposed to two experimental conditions: the placebo (PL) and 6 mg of bromazepam (Br 6 mg), following a randomized, double-blind design on different days. Specifically, we observe absolute power extracted from qEEG data for theta band. We expected to see a decrease in absolute theta power in the temporal and parietal areas due to the influence of bromazepam for the experimental group when compared with the placebo group. We found a main effect for the condition factor for electrodes T3, T4, P3 and P4. We also observed a main effect for the period factor for electrodes P3 and P4. We observed that the ingestion of 6 mg of bromazepam induces different patterns in theta power at the temporal and parietal sites. We concluded that 6 mg of bromazepam was an important factor in the fluctuation of the activities in the temporal and parietal areas. We then hypothesize about the specific role of this drug during the execution of a visuomotor task and within the sensorimotor integration process. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

Responsiveness of sensorimotor cortex during pharmacological intervention with bromazepam

The aim of this study was to investigate the influence of bromazepam on EEG and the motor learning process when healthy subjects were submitted to a typewriting task. We investigated bromazepam due to its abuse by various populations and its prevalent clinical use among older individuals which are more sensitive to the negative effects of long half-life benzodiazepines. A randomized double-blind design was used with subjects divided into three groups: placebo (n = 13), bromazepam 3 mg (n = 13) and bromazepam 6 mg (n = 13). EEG data comprising theta, alpha and beta bands was recorded before, during and after the motor task. Our results showed a lower relative power value in the theta band in the Br 6 mg group when compared with PL. We also observed a reduction in relative power in the beta band in the Br 3 mg and Br 6 mg when compared with PL group. These findings suggest that Br can contribute to a reduced working memory load in areas related to attention processes. On the other hand, it produces a higher cortical activation in areas associated with sensory integration. Such areas are responsible for accomplishing the motor learning task. The results are an example of the usefulness of integrating electrophysiological data, sensorimotor activity and a pharmacological approach to aid in our understanding of cerebral changes produced by external agents. (c) 2008 Elsevier Ireland Ltd. All rights reserved.

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Detection and description of non-linear interdependence in normal multichannel human EEG data

Objectives: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. Methods: Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. Results: Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. Conclusions: Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Uma contribuição ao estudo de sinais de EEG para avaliar estados emocionais e mentais de crianças com autismo na interação com robô móvel

O Transtorno do Espectro do Autismo (TEA) caracteriza-se por uma série de distúrbios cognitivos e neurocomportamentais e sua prevalência mundial é estimada em 1 criança com TEA a cada 160 crianças com típico desenvolvimento (TD). Indivíduos com TEA apresentam dificuldade em interpretar as emoções alheias e em expressar sentimentos. As emoções podem ser associadas à manifestação de sinais fisiológicos, e, dentre eles, os sinais cerebrais têm sido muito abordados. A detecção dos sinais cerebrais de crianças com TEA pode ser benéfica para o esclarecimento de suas emoções e expressões. Atualmente, muitas pesquisas integram a robótica ao tratamento pedagógico do TEA, através da interação com crianças com esse transtorno, estimulando habilidades sociais, como a imitação e a comunicação. A avaliação dos estados mentais de crianças com TEA durante a sua interação com um robô móvel é promissora e assume um aspecto inovador. Assim, os objetivos deste trabalho foram captar sinais cerebrais de crianças com TEA e de crianças com TD, como grupo controle, para o estudo de seus estados emocionais e para avaliar seus estados mentais durante a interação com um robô móvel, e avaliar também a interação dessas crianças com o robô, através de escalas quantitativas. A técnica de registro dos sinais cerebrais escolhida foi a eletroencefalografia (EEG), a qual utiliza eletrodos colocados de forma não invasiva e não dolorosa sobre o couro cabeludo da criança. Os métodos para avaliar a eficiência do uso da robótica nessa interação foram baseados em duas escalas internacionais quantitativas: Escala de Alcance de Metas (do inglês Goal Attainment Scaling - GAS) e Escala de Usabilidade de Sistemas (do inglês System Usability Scale - SUS). Os resultados obtidos mostraram que, pela técnica de EEG, foi possível classificar os estados emocionais de crianças com TD e com TEA e analisar a atividade cerebral durante o início da interação com o robô, através dos ritmos alfa e beta. Com as avaliações GAS e SUS, verificou-se que o robô móvel pode ser considerado uma potencial ferramenta terapêutica para crianças com TEA.